Resilient infrastructure refers to physical and digital systems that can withstand, adapt to, and recover from disruptions such as natural disasters, cyberattacks, or operational failures. In modern engineering and urban planning, digital twins—virtual representations of physical assets—are becoming a central tool in building resilience. A digital twin can model real-time behavior, simulate scenarios, and predict failures before they occur. However, as these models grow more complex and data-driven, ensuring their accuracy, security, and reliability becomes a challenge. Artificial Intelligence (AI)–based auditing offers a powerful approach to verifying and enhancing the trustworthiness of digital twins.

Digital Twins in Resilient Infrastructure

A digital twin is a dynamic, data-integrated model that mirrors the state, behavior, and performance of an actual physical system. In infrastructure, this could mean a twin of a bridge, a power grid, a water treatment facility, or even an entire city. The twin receives continuous streams of sensor data, which allows it to simulate responses to stressors, forecast maintenance needs, and optimize operations.

For resilience, digital twins enable:

Predictive maintenance: Identifying component degradation before failure.
Disaster preparedness: Simulating earthquakes, floods, or cyber intrusions to test response strategies.
Operational optimization: Adjusting resource use during high demand or emergencies.

However, digital twins are only as good as the data and algorithms that power them. If the twin’s model is inaccurate or its data feed is compromised, decision-making could be misinformed, leading to dangerous consequences. This is where AI-driven auditing becomes crucial.

The Role of AI in Auditing Digital Twins

Auditing in the context of digital twins involves examining model fidelity, data integrity, security posture, and operational performance. Traditional auditing methods are often manual, slow, and unable to handle the vast data complexity of modern infrastructure systems. AI brings automation, scalability, and advanced pattern recognition to the process.

Key AI functions in auditing digital twins include:

Data Integrity Verification
AI algorithms can detect anomalies in sensor data streams that may indicate sensor malfunctions, calibration drift, or malicious tampering. Machine learning models can flag deviations from expected patterns, reducing the risk of acting on corrupted or falsified data.
Model Fidelity Assessment
Over time, physical systems evolve—components wear, upgrades occur, environmental conditions change. AI can compare live operational data with the digital twin’s outputs to identify when the model starts diverging from reality. Automated retraining processes can then recalibrate the twin.
Cybersecurity Auditing
Digital twins are networked systems, making them vulnerable to cyberattacks. AI-powered intrusion detection systems can analyze traffic patterns, detect unusual access attempts, and assess the potential impact of breaches on the twin’s data and control systems.
Scenario Validation
AI can rapidly run thousands of stress-test simulations across a range of potential disaster conditions to evaluate the twin’s resilience strategies. This continuous validation ensures the twin remains reliable for decision support under changing risk profiles.
Ethics and Bias Detection
For infrastructure that impacts public welfare, AI auditing can also review decision-making algorithms within the twin for embedded biases, ensuring equitable and fair outcomes in automated responses.

EQ.1.Scenario validation and resilience metric:

Benefits of AI-Driven Auditing for Resilience

Integrating AI into the auditing process for digital twins provides several critical advantages:

Continuous Assurance: Unlike periodic manual audits, AI systems operate in real time, providing ongoing verification and instant alerts.
Scalability: AI can handle the massive data streams generated by complex infrastructure systems without performance bottlenecks.
Predictive Insight: Beyond detecting problems, AI can forecast emerging risks, giving operators time to intervene before disruptions occur.
Adaptability: AI auditing systems can learn from historical incidents and continuously refine their detection and analysis capabilities.

These benefits directly support resilient infrastructure goals by ensuring that the digital twin remains accurate, secure, and effective over its lifecycle.

Challenges and Limitations

While AI auditing offers powerful capabilities, several challenges remain:

Data Quality Dependence: If input data is incomplete or biased, AI may produce misleading audit results.
Model Transparency: Complex AI algorithms, especially deep learning models, may be difficult to interpret, complicating trust and regulatory compliance.
Integration Complexity: Retrofitting AI auditing systems into existing infrastructure digital twins may require substantial technical and organizational changes.
Resource Requirements: Real-time AI auditing demands significant computing resources and robust cybersecurity measures, which can be costly.

Overcoming these challenges requires strategic planning, stakeholder collaboration, and careful selection of AI technologies.

EQ.2. Control optimization under audited model (MPC objective):

Future Directions

The future of resilient infrastructure will likely see deeper integration between AI and digital twin auditing, driven by advances in both fields. Potential developments include:

Self-Healing Digital Twins: Systems that not only detect discrepancies but automatically adjust models and controls to correct them without human intervention.
Federated Auditing: Distributed AI auditing models that allow multiple stakeholders (e.g., city agencies, private operators) to collaborate without sharing raw data, enhancing privacy and security.
Explainable AI for Auditing: Methods that provide transparent reasoning behind audit findings, improving trust and regulatory acceptance.
Multi-Layer Resilience Testing: AI-driven simulations that simultaneously evaluate physical, digital, and social resilience in interconnected infrastructure systems.

These trends will expand the capability of digital twins from being passive mirrors of reality to becoming proactive guardians of infrastructure resilience.

Conclusion

Digital twins are transforming how infrastructure is monitored, managed, and protected. However, their value depends on their accuracy, security, and adaptability in the face of evolving risks. AI-driven auditing offers a robust approach to ensuring that digital twins remain trustworthy tools for resilience. By continuously validating data integrity, model fidelity, cybersecurity, and ethical performance, AI auditing supports infrastructure systems that can withstand and recover from disruptions. As infrastructure networks become more complex and interdependent, the synergy between AI and digital twins will be a cornerstone of resilient design and operation.

Resilient Infrastructure: Auditing Digital Twins with AI